Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression
Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient descent and per-problem hyperparameter tuning of continuous-optimization methods are poorly suited to two practically important regimes: the sample-limited regime, where the number of samples is comparable to or smaller than the number of nodes in the DAG, and the compute-limited regime. This work proposes SURE-Ridge, a non-iterative, closed-form estimator for equal variance linear Gaussian SEM. The method performs parallel node-wise regressions with regularization parameters chosen adaptively by Stein's unbiased risk est...
| Authors | Sambit Mishra, Urbashi Mitra |
|---|---|
| Primary category | cs.LG |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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